Load Dynamics Research Articles

Automatic Generation Control in Renewables-Integrated Multi-Area Power Systems: A Comparative Control Analysis

Electrical load dynamics result in system instability if not met with adequate power generation. Therefore, monitoring and control plans are necessary to avoid potential consequences. Tie-line-bias control has facilitated power exchange between interconnected areas to cope with load dynamics. However, this approach presents a challenge, as load variation in either area leads to frequency deviations and power irregularities in each of the interconnected areas, which is undesirable. The load frequency control loop method is used to address this issue, which utilizes area control errors. This study focuses on the control of inter-area oscillations in a six-area power system under the effect of renewable energy sources. It evaluates the area control errors in response to changes in load and the penetration of renewable energy into the system. To mitigate these errors efficiently, an adaptive-PID controller is proposed, and its results are compared with PI and PID controllers optimized with heuristic and meta-heuristic algorithms. The findings demonstrate the superiority of the proposed controller over traditional controllers in mitigating tie-line power errors and frequency deviations in each area of the interconnected power system, thus helping to mitigate inter-area oscillations and restore system stability.

A Multivariable, Adaptive, Robust, Primary Control Enforcing Predetermined Dynamics of Interest in Islanded Microgrids Based on Grid-Forming Inverter-Based Resources

This paper proposes a multivariable, adaptive, robust (MAR) control strategy for islanded inverter-based resources (IBRs) operating as grid-forming inverters. The proposed method is employed in the inner control loop of the primary layer in the hierarchical or decentralized structures for the islanded operation of microgrids. The MAR control scheme is responsible for stabilizing IBRs’ output voltage in autonomous operations of microgrids, considering mismatched input voltage disturbances from the grid side and a large amount of system uncertainty. The control methodology introduced in this paper does not rely on the system’s physical parameters, such as microgrid topology, load dynamics, LCL filters, and output connectors. As a result, there is no need to know the nominal values or the bounds of uncertainties in system dynamics. The MAR control method uses online adaptation rules first to identify and then adjust the control parameters of the closed-loop system based on an arbitrary dynamic model. In other words, the MAR method replaces the actual dynamics of IBRs with predetermined dynamics of interest. Simulation results in the MATLAB/Simulink environment confirm the capability of the scheme introduced for the closed-loop stabilization and voltage regulation in the presence of disturbances and a significant amount of uncertainty under various case studies; moreover, comparative simulations by comparing the presented method with other studies using sliding mode control are provided. Finally, experiments verify the effectiveness and practicality of the proposed MAR control scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Inverter-based resources are integral parts of current and especially future power and energy systems; with increasing concerns about carbon footprints, the tendency to substitute traditional synchronous generators with inverter-based resources increases. This transition towards the widespread use of power electronics devices needs careful studies regarding the stability and control of power converters. Although existing studies are addressing potential control system challenges, they suffer from complex mathematical computations and the need for the system’s preliminary information. With this in mind, this study proposes a multivariable, adaptive, robust control strategy for the inner voltage control loop of grid-forming inverters. This method utilizes online estimation algorithms to identify inverter-based resources’ parameters and tune control system parameters simultaneously, making it applicable even to cases with slow parameter variations caused by aging or environmental changes. It can compensate for potential voltage disturbances from the grid side and enable the designer to replace undesirable dynamics of inverter-based resource units with arbitrary and stable dynamics of interest. In fact, unlike traditional methods that need control parameters and gains to be tuned to achieve a proper dynamic response, the control system designer can choose reference dynamics and enforce the closed-loop system to imitate the dynamical model selected. This model is usually chosen based on established priorities, such as response time and other transient behaviors. Moreover, this method does not require complex mathematical and algebraic calculations to design and implement. It can be easily applied to inverter-based resource units after selecting the desired reference dynamics, as shown through this study’s experimental result. The above points give this method a competitive edge over the existing algorithms, especially in practical applications.

TTV and CMV viral load dynamics: Which emerges first during immunosuppression?

Novel biomarkers reflecting the degree of immunosuppression in transplant patients are required to ensure eventual personalized equilibrium between rejection and infection risks.With the above aim, Torque Teno Virus(TTV) viremia was precisely examined in a large cohort of transplanted immunocompromised patients (192 hematological and 60 solid organ transplant recipients) being monitored for Cytomegalovirus reactivation.TTV load was measured in 2612 plasma samples from 448 patients. The results revealed a significant increase in TTV viral load approximately 14 days following CMV reactivation/infection in solid organ transplant(SOT) patients. No recognizable difference in TTV load was noted among hematological patients during the entire timeframe analyzed. Furthermore, a temporal gap of approximately 30 days was noted between the viral load peaks reached by the two viruses, with Cytomegalovirus(CMV) preceding TTV.It was not possible to establish a correlation between CMV reactivation/infection and TTV viremia in hematological patients. On the other hand, the SOT patient cohort allowed us to analyze viral kinetics and draw intriguing conclusions. Taken together, the data suggest, to our knowledge for the first time, that CMV infection itself could potentially cause an increase in TTV load in the peripheral blood of patients undergoing immunosuppressive therapy.

In vivo Labeling and Intravital Imaging of Bacterial Infection using a Near-infrared Fluorescent D-Amino Acid Probe.

Bacterial infections still pose a severe threat to public health, necessitating novel tools for real-time analysis of microbial behaviors in living organisms. While genetically engineered strains with fluorescent or luminescent reporters are commonly used in tracking bacteria, their in vivo uses are often limited. Here, we report a near-infrared fluorescent D-amino acid (FDAA) probe, Cy7ADA, for in situ labeling and intravital imaging of bacterial infections in mice. Cy7ADA probe effectively labels various bacteria in vitro and pathogenic Staphylococcus aureus in mice after intraperitoneal injection. Because of Cy7's high tissue penetration and the quick excretion of free probes via urine, real-time visualization of the pathogens in a liver abscess model via intravital confocal microscopy is achieved. The biodistributions, including their intracellular localization within Kupffer cells, are revealed. Monitoring bacterial responses to antibiotics also demonstrates Cy7ADA's capability to reflect the bacterial load dynamics within the host. Furthermore, Cy7ADA facilitates three-dimensional pathogen imaging in tissue-cleared liver samples, showcasing its potential for studying the biogeography of microbes in different organs. Integrating near-infrared FDAA probes with intravital microscopy holds promise for wide applications in studying bacterial infections in vivo.

IL-10 predicts incident neuroinflammatory disease and proviral load dynamics in a large Brazilian cohort of people living with human T-lymphotropic virus type 1.

Human T-Lymphotropic Virus type-1 (HTLV-1) is a unique retrovirus associated with both leukemogenesis and a specific neuroinflammatory condition known as HTLV-1-Associated Myelopathy (HAM). Currently, most proposed HAM biomarkers require invasive CSF sampling, which is not suitable for large cohorts or repeated prospective screening. To identify non-invasive biomarkers for incident HAM in a large Brazilian cohort of PLwHTLV-1 (n=615 with 6,673 person-years of clinical follow-up), we selected all plasma samples available at the time of entry in the cohort (between 1997-2019), in which up to 43 cytokines/chemokines and immune mediators were measured. Thus, we selected 110 People Living with HTLV-1 (PLwHTLV-1), of which 68 were neurologically asymptomatic (AS) at baseline and 42 HAM patients. Nine incident HAM cases were identified among 68 AS during follow-up. Using multivariate logistic regression, we found that lower IL-10, IL-4 and female sex were independent predictors of clinical progression to definite HAM (AUROC 0.91), and outperformed previously suggested biomarkers age, sex and proviral load (AUROC 0.77). Moreover, baseline IL-10 significantly predicted proviral load dynamics at follow-up in all PLwHTLV-1. In an exploratory analysis, we identified additional plasma biomarkers which were able to discriminate iHAM from either AS (IL6Rα, IL-27) or HAM (IL-29/IFN-λ1, Osteopontin, and TNFR2). In conclusion, female sex and low anti-inflammatory IL-10 and IL-4 are independent risk factors for incident HAM in PLwHTLV-1,while proviral load is not, in agreement with IL-10 being upstream of proviral load dynamics. Additional candidate biomarkers IL-29/IL-6R/TNFR2 represent plausible therapeutic targets for future clinical trials in HAM patients.

Infection dynamics and shedding kinetics of RSIV in red sea bream (Pagrus major) using immersion and cohabitation challenge models

Red sea bream iridovirus (RSIV) poses a significant threat to global aquaculture and wild fish populations, leading to substantial economic and ecological losses. Despite its recognition as a major viral disease, our understanding of the dynamics of RSIV infection and transmission in naturalistic conditions remains limited. Therefore, the present study aimed to investigate the kinetics, shedding, and interspecies transmission of RSIV in red sea bream (Pagrus major) through immersion and cohabitation challenge models. The primary objectives were to (1) elucidate the pathogenicity of RSIV in red sea bream through naturalistic immersion challenges, (2) analyze the viral load and shedding dynamics in RSIV-infected fish, and (3) assess the risk of horizontal transmission to other species. Our results indicated that red sea bream exposed to high doses of RSIV (101–107 RSIV copies/mL) experienced rapid cumulative mortality, particularly at 25 °C. Notably, red sea bream infected by immersion at 25 and 15 °C showed a significant increase in RSIV shedding into seawater when the viral load exceeded approximately 102.48 RSIV copies/mg, highlighting a critical threshold for understanding disease spread in aquaculture settings. Histopathological analysis revealed that the spleen and kidney were primarily affected, with significant lesion scores correlating with viral loads. We observed a strong correlation between viral load and histopathological infection grades, particularly in these primary tissues. Furthermore, cohabitation experiments highlighted the risk of RSIV transmission to other fish species, including rock bream (Oplegnathus fasciatus) and flathead grey mullet (Mugil cephalus), indicating the possibility of horizontal spread in multi-species farming systems. These findings emphasize the importance of continuous surveillance and robust biosecurity measures in aquaculture systems to manage RSIV infection effectively. Overall, this study contributes to understanding RSIV dynamics in red sea bream, offering insights into the impact of this virus in aquaculture environments, particularly concerning viral shedding and transmission.

Influence of test paradigm on loading dynamics during proximal femur fracture tests simulating sideways falls

Fall-related hip fractures are a serious public health issue in older adults. As most mechanistic hip fracture risk prediction models incorporate tissue tolerance, test methods that can accurately characterize the fracture force of the femur (and factors that influence it) are imperative. While bone possesses viscoelastic properties, experimental characterization of rate-dependencies has been inconsistent in the whole-femur literature. The goal of this study was to investigate the influence of experimental paradigm on loading rate and fracture force (both means and variability) during mechanical tests simulating lateral fall loadings on the proximal femur. Six pairs of matched femurs were split randomly between two test paradigms: a ‘lower rate’ materials testing system (MTS) with a constant displacement rate of 60 mm/s, and a hip impact test system (HIT) comprised of a custom-built vertical drop tower utilizing an impact velocity of 4 m/s. The loading rate was 88-fold higher for the HIT (mean (SD) = 2465.49 (807.38) kN/s) compared to the MTS (27.78 (10.03) kN/s) paradigm. However, no difference in fracture force was observed between test paradigms (mean (SD) = 4096.4 (1272.6) N for HIT, and 3641.3 (1285.8) N for MTS). Within-paradigm variability was not significantly different across paradigms for either loading rate or fracture force (coefficients of variation ranging from 0.311 to 0.361). Within each test paradigm, significant positive relationships were observed between loading rate and fracture force (HIT adjusted R2 = 0.833, p = 0.007; MTS adjusted R2 = 0.983, p < 0.0001). Overall, this study provides evidence that energy-based impact simulators can be a valid method to measure femoral bone strength in the context of fall-related hip fractures. This study motivates future research to characterize potential non-linear relationships between loading rate and fracture threshold at both macro and microscales.

Design of digital compensators for multi-output DC-DC converters with reduced cross-regulation

ABSTRACT In the recent times, a single multi-port DC-DC converter (MPC) is highly preferred over the multiple single output DC-DC converters. Single Input Dual Output (SIDO) Buck converter with Coupled Inductors (CI) are widely employed for the reduction of multiple outputs. In this paper, the prognostic procedures to monitor MPC operating with load dynamics are discussed. The occurrence of cross-regulation during the load dynamics is suppressed by an improved digital compensator, which was specifically designed to address the cross-regulation problem. The developed digital compensation circuit is employed in the CI-SIDO Buck converter with an input of 48 V and delivering outputs of 12 V/75Watts and 24 V/125 Watts. The occurrence of cross-regulation is controlled by decoupled dual loop digital PID controller with dynamical digital compensation. A meticulous Frequency Response Analysis (FRA) with bode plot is conducted exhaustively for the derived system transfer functions. The proposed design is verified in simulation and experiments, exhibiting an enhanced results with significant suppression in cross-regulation and stability.

Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction

Visual highlighting can guide user attention in complex interfaces. However, its effectiveness under limited attentional capacities is underexplored. This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. Our analysis, using eye-movement data from 27 participants viewing 150 unique webpages reveals that while participants' ability to attend to UI elements decreases with increasing cognitive load, dynamic adaptations (i.e., highlighting) remain attention-grabbing. The presence of these factors significantly alters what people attend to and thus what is salient. Accordingly, we show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads. Our empirical insights, along with our openly available dataset, enhance our understanding of attentional processes in UIs under varying cognitive (and perceptual) loads and open the door for new models that can predict user attention while multitasking.

The seasonal-trend disentangle based prognostic framework for PEM fuel cells

Prognostics and Health Management (PHM) offer a potential solution to improve the durability of Proton Exchange Membrane Fuel Cells (PEMFCs), where degradation prognostic is the key component of the PHM system. However, limited by the dynamic operating environment, accurate and efficient prognostic algorithms still required for real applications. In this paper, the Seasonal-Trend Disentangle (STD) based prognostic framework is proposed to enhance the Remaining Useful Life (RUL) prediction of PEMFC system. Specifically, a lightweight parameter estimation neural network is introduced to estimate the aging parameters of the FC degradation model directly from PEMFC measurements. Besides, the integral of the FC degradation model at the specified current density range serves as the comprehensive Health Index (HI), which could combine different aging parameters and mitigate the effect of load dynamics. The STD-based prognostic model effectively captures the time-invariant features in the HI sequence from trend and seasonal phases, improving Remaining Useful Life (RUL) prediction performance. The proposed prognostic model is compared with RNN-family, ESN, and Dilated CNN methods using three distinct durability datasets from FCs operating under static, quasi-dynamic, and cyclically varying dynamic current loads. Experimental results demonstrate that the proposed algorithm outperforms the reference methods in terms of RMSE and MAPE metrics in the aging prognostic, and the proposed method could obtain satisfactory RUL prediction results 100–150 h earlier than the reference methods.

Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors

In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019–2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model—with random slopes and intercepts by year—, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases. Significance of the studyThis study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune responses, opening avenues for further research into adaptive changes and responses to environmental shifts in the context of HIV infection. The study's emphasis on molecular characteristics as predictors of pVL adds valuable insights to epidemiological and evolutionary studies of viruses, providing new avenues for understanding and managing HIV infection at the population level.

Rapid seagrass meadow expansion in an Indian Ocean bright spot

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000–2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km2 year−1, r2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass ‘bright spot’ highly worthy of increased attention across scientific, commercial, and conservation policy contexts.

Light‐Responsive Nanoantennas Integrated into Nanoscale Metal–Organic Frameworks for Photothermal Drug Delivery

Nanoscale metal–organic frameworks (NMOFs) exhibit unique properties for drug delivery, including ultrahigh storage capabilities, biocompatibility, biodegradability, and sustained release of encapsulated cargo. However, due to their localized electronic states, MOFs are nonresponsive to external stimuli such as light or magnetic fields. This study investigates the integration of light‐responsive nanoantennas into NMOFs to enhance their application as smart drug delivery nanosystems. By integrating gold bipyramid nanoantennas within ZIF‐8 and NU‐1000 NMOFs, core@shell nanosystems are created with photothermal capabilities. Utilizing cresyl violet as a model drug, the loading and release dynamics of these nanosystems are analyzed, demonstrating controlled drug release under near‐infrared (NIR) light stimulation. Photothermal release studies conducted in living cells reveal the potential of these nanocomposites for spatiotemporal targeted, light‐activated drug delivery. Further evaluation of the NU‐1000 nanocomposite loaded with chemotherapeutics—doxorubicin, carboplatin, and oxaliplatin—in both 2D and 3D cell cultures shows the nanosystem effectiveness in cell internalization and therapeutic NIR activation. The findings demonstrate that the incorporation of stimuli‐responsive elements into NMOFs offers a promising approach for developing advanced drug delivery platforms.

Study of the Level of Sports Addiction Among Students at the Faculty of Sports Science

The aim of this study was to investigate the exercise addiction of the students of the School of Physical Education and Sport and to determine the differences according to the type of exercise, age of exercise and load dynamics. The Exercise Addiction Scale (EBÖ-21) developed by Housenblas and Downs (2002) and the Turkish Validity and Reliability Study by Yeltepe and İkizler (2007) and the Personal Information Form were applied to the sample group. The sample of the study consisted of 245 athletes who regularly train at Afyon Kocatepe University School of Physical Education and Sports. Studies on this subject indicate that addiction to sports is similar to addiction to chemical substances due to the presence of withdrawal and tolerance symptoms. The results of the study presented show that self-confidence and athletic performance increase as the students' level increases. When evaluating exercise addiction by gender, it was found that female students were more likely than male students to consider physical problems and show self-control and interruption of exercise behaviour. When evaluated by age, exercise addiction was found to have decreased and been brought under control in older individuals, indicating progress in self-control of addiction.

Piece-Wise Droop Controller for Enhanced Stability in DC-Microgrid-Based Electric Vehicle Fast Charging Station

The need for public fast electric vehicle charging station (FEVCS) infrastructure is growing to meet the zero-emission goals of the transportation sector. However, the large charging demand of the EV fleet may adversely impact the grid’s stability and reliability. To improve grid stability and reliability, the development of a DC microgrid (MG) leveraging renewable energy sources to supply the energy demands of FEVCSs is the sustainable solution. Balancing the intermittent EV charging demand and fluctuating renewable energy generation with the stable DC bus voltage of a DC MG is a challenging objective. To address this objective, a piece-wise droop control strategy is proposed in this work. The proposed scheme regulates DC bus voltage and power sharing with droop value updating in a region-based load current distribution. Voltage compensation in individual regions is carried out to further improve the degree of freedom. In this paper, the performance of the proposed strategy is evaluated with the consideration of real-time solar PV dynamics and EV load dynamics. Further, to showcase the effectiveness of the proposed strategy, a comparative analysis with a maximum power point tracking (MPPT) controller against various dynamic EV load scenarios is carried out, and the results are validated through a hardware-in-loop experimental setup. Despite the intermittent source and EV load dynamics, the proposed piece-wise droop control can maintain voltage regulation with less than 1% deviation.

A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor’s Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future.

Lead-Acid Battery and Supercapacitor Based Hybrid Energy Storage Systems in Microgrid for Energy Control System

Lead-acid batteries are a common energy storage option in modern microgrid applications. This study suggests installing an Energy Management System (EMS) that is managed by a hybrid energy storage system (HESS) consisting of lead-acid batteries and supercapacitors (SCs). Lower operating costs and longer battery life are the goals. Lead acid battery state of charge (SOC) depletion and daily cycle stress are reduced by SCs, which control and supply the high-frequency, power-dense section of the load cycle. But it's crucial to size the HESS correctly, taking into account load demand, available power sources, and budgetary restrictions. This article explains an algorithm that outlines the HESS's energy management control. One of the main advantages of using HESS versus passive systems is that it reduces the overall size of the system. By estimating future load dynamics using load current, the proposed energy management system primes the system for impending changes.

Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization.

Combination of Doxorubicin and Antiangiogenic Agents in Drug-Eluting Beads: In Vitro Loading and Release Dynamics in View of a Novel Therapeutic Approach for Hepatocellular Carcinoma.

Antiangiogenic agents have been used for many years as a first-line systemic treatment for advanced HCC. Embolization with cytostatic drugs on the other hand is the first-line treatment for intermediate HCC. The two types of drugs have not been combined for intraarterial delivery yet. The loading and release dynamics and the in vitro effect of their combination are tested in this experimental study. Drug-eluting beads were loaded with doxorubicin, sunitinib and sunitinib analogue piperazine (SAP) alone and with their combinations. Diameter change, loading, release, and effect in cellular proliferation were assessed. The average microsphere diameter after loading was 473.7µm (μm) for Doxorubicin, 388.4μm for Sunitinib, 515.5μm for SAP, 414.8μm for the combination Doxorubicin/Sunitinib and 468.8μm for the combination Doxorubicin /SAP. Drug release in 0.9%NaCl was 10% for Doxorubicin, 49% for Sunitinib, 25% for SAP, 20%/18% for the combination Doxorubicin/Sunitinib, and 18%/23% for the combination Doxorubicin/SAP whereas in human plasma it was 56%, 27%, 13%, 76%/63% and 62%/15%, respectively. The mean concentration of Doxorubicin that led to inhibition of 50% of cellular proliferation in an HCC Huh7 cell line was 163.1nM (nM), for Sunitinib 10.3 micromolar (μΜ), for SAP 16.7μΜ, for Doxorubicin/Sunitinib 222.4nM and for Doxorubicin/SAP 275nM. Doxorubicin may be combined with antiangiogenic drugs with satisfactory in vitro loading and release outcomes and effect on cellular lines.

Site Quality Models and Fuel Load Dynamic Equation Systems Disaggregated by Size Fractions and Vegetative States in Gorse and High Heath Shrublands in Galicia (NW Spain)

Compatible model systems were developed for estimating fuel load dynamics in Ulex europaeus (gorse) and in Erica australis (Spanish heath) dominated shrub communities at stand level. The models were based on intensive, detailed destructive field sampling and were fitted simultaneously to fulfill the additivity principle. The models enable, for the first time, estimation of the biomass dynamics of the total shrub layer, size fractions and vegetative stage, with reasonably good accuracy. The approach used addresses the high variability in shrub biomass estimates by using a site index (SI) based on biomass levels at a reference age of 10 years. Analysis of the effect of climatic variables on site index confirmed the preference of gorse for mild temperatures and the ability of high heath communities to tolerate a wider range of temperatures. In the gorse communities, SI tended to increase as summer rainfall and the mean temperature of the coldest month increased. However, in the heath communities, no relationships were observed between SI and any of the climatic variables analyzed. The study findings may be useful for assessing and monitoring fuel hazards, updating fuel mapping, planning and implementing fuel reduction treatments and predicting fire behavior, among other important ecological and biomass use-related applications.